AI Observability for LLMOps: Run LLMs in Production with Confidence
We design and operate end-to-end monitoring, tracing, evaluation, and governance for LLM-based systems in production. Our engineers instrument prompts, models, retrieval, tools, and agents so your teams can detect regressions, debug incidents, control cost, and prove compliance across cloud-native AIOps platforms on AWS, Azure, and GCP.
Get a single, governed control plane for every LLM call, agent decision, and cost event across environments.
- Full-stack LLM tracing: prompts, tools, retrieval chunks, tokens, latency, and cost per request
- Online and offline evals with regression gates tied to CI/CD
- Drift, hallucination, and anomaly detection with alerting and incident runbooks
- Guardrails, PII redaction, and audit logs for GDPR/HIPAA-ready deployments
- Reference integrations with Datadog, OpenTelemetry, Langfuse, Arize, and native cloud AIOps monitoring
Why Do LLM Systems Fail Silently in Production?
Most enterprises ship a working GenAI prototype, then lose control once traffic, tools, and use cases grow. Without AI observability and a real LLMOps framework, teams cannot explain regressions, prove compliance, or contain spend. Failures stay silent: answers degrade, agents loop, costs spike, and nobody knows until a customer complains.
Architecture & Technical Building Blocks
Routing, rate limits, fallback, and token accounting per tenant and feature.
Traces across prompts, retrievers, tools, and every agent step.
Versioned in Git with CI/CD gates and golden datasets.
LLM-as-judge, regex, and classifier-based checks at inference time.
Monitoring on groundedness, toxicity, latency, and cost.
Datadog, Langfuse, Arize, Grafana, AWS CloudWatch, Azure Monitor, and SIEM.
PII redaction, data residency, and per-tenant isolation.
How We Deliver: From Discovery to Run
We audit your current LLM stack, AIOps capabilities, SLOs, and compliance scope. Output: target architecture, observability gap analysis, and a prioritized LLMOps backlog. (1-2 weeks)
We deploy the model gateway, tracing, prompt/eval registry, dashboards, and guardrails in your cloud. Output: a production-grade AI observability stack integrated with your existing AIOps monitoring tools. (3-5 weeks)
We wire evals into CI/CD, define incident runbooks, and run load and red-team tests. Output: the first LLM workload live under full observability, with SLOs and alerts. (2-3 weeks)
We provide SLA-based support, AIOps training for your engineers, and quarterly reviews. Output: your teams own the platform, extend it to new use cases, and report quality, cost, and compliance metrics to the business. (ongoing)
Benefits of Production-Grade AI Observability
40-70% faster incident resolution with end-to-end tracing and AIOps for incident management.
20-40% lower token and inference cost through routing, caching, and cost attribution.
3-5x more release frequency for prompts and models with CI/CD-integrated evals.
>90% detection rate on quality regressions before they hit end users.
Who This Service Is For
LLMOps & AI Observability Platform Engineering
Five capabilities, from the control plane to incident automation, that take LLM systems from a working PoC to a governed production platform.
Frequently Asked Questions
AI observability captures traces, metrics, evaluations, and outputs from AI systems, while AIOps monitoring focuses on infrastructure and application telemetry. You need both: AIOps observability covers CPU, latency, and errors; AI observability adds prompts, tokens, retrieval context, tool calls, groundedness, and hallucination signals that classical AIOps monitoring tools do not capture.
Yes. Datadog AIOps and similar products are strong for infrastructure and APM, but LLMOps adds prompt versioning, eval pipelines, guardrails, and model governance. We typically integrate LLMOps tooling (Langfuse, Arize, OpenTelemetry) with your existing AIOps platform so you keep one pane of glass instead of replacing your vendor.
We combine three layers: offline evals on golden datasets in CI/CD, online LLM-as-judge and classifier-based scoring at inference time, and AIOps anomaly detection on metrics like groundedness, citation rate, and user feedback. Regressions trigger alerts, auto-rollback, or prompt quarantine depending on severity.
Yes. Our LLMOps architecture is cloud-native and vendor-neutral. We deploy on AWS, Azure, or GCP using OpenTelemetry, a model gateway, and portable components. You can switch model providers, observability backends, or AIOps tools without rewriting your applications.
Most clients see measurable results within 6-8 weeks: full tracing on the first production workload, cost attribution per feature, and a working eval pipeline. Within one quarter, LLM incident MTTR typically drops 40-70% and teams ship prompt and model changes with confidence.
Yes. Every engagement includes AIOps training: hands-on workshops on LLMOps, observability, evals, and incident management, plus pairing sessions during rollout. The goal is that your platform and SRE teams fully own the stack after go-live, with us providing SLA-based support as needed.
We build GDPR/HIPAA-ready controls into the platform: PII redaction before external API calls, encryption, IAM/RBAC, audit logs for every prompt and output, data residency options, and approval workflows for model and prompt changes. These controls map directly to EU AI Act requirements for high-risk AI systems.
Ready to Put Your LLMs Under Real Observability?
Book a 30-minute, no-obligation technical review. We will assess your current LLM stack, AIOps capabilities, and observability gaps, then give you a concrete roadmap to production-grade LLMOps. No slides, just architecture.
Technical review
A 30-minute, no-obligation call to assess your LLM stack, AIOps capabilities, and observability gaps.
Roadmap
You get a concrete plan to reach production-grade LLMOps, with target architecture and a prioritized backlog.
Build & go-live
We implement the platform, harden it, and put your first LLM workload live under full observability.